Forecasting the strength of graphene nanoparticles-reinforced cementitious composites using ensemble learning algorithms

Results in Engineering, Год журнала: 2024, Номер 21, С. 101837 - 101837

Опубликована: Фев. 6, 2024

Contemporary infrastructure requires structural elements with enhanced mechanical strength and durability. Integrating nanomaterials into concrete is a promising solution to improve However, the intricacies of such nanoscale cementitious composites are highly complex. Traditional regression models encounter limitations in capturing these intricate compositions provide accurate reliable estimations. This study focuses on developing robust prediction for compressive (CS) graphene nanoparticle-reinforced (GrNCC) through machine learning (ML) algorithms. Three ML models, bagging regressor (BR), decision tree (DT), AdaBoost (AR), were employed predict CS based comprehensive dataset 172 experimental values. Seven input parameters, including graphite nanoparticle (GrN) diameter, water-to-cement ratio (wc), GrN content (GC), ultrasonication (US), sand (SC), curing age (CA), thickness (GT), considered. The trained 70 % data, remaining 30 data was used testing models. Statistical metrics as mean absolute error (MAE), root square (RMSE) correlation coefficient (R) assess predictive accuracy DT AR demonstrated exceptional accuracy, yielding high coefficients 0.983 0.979 training, 0.873 0.822 testing, respectively. Shapley Additive exPlanation (SHAP) analysis highlighted influential role positively impacting CS, while an increased (w/c) negatively affected CS. showcases efficacy techniques accurately predicting nanoparticle-modified concrete, offering swift cost-effective approach assessing nanomaterial impact reducing reliance time-consuming expensive experiments.

Язык: Английский

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Metaheuristic optimization algorithms-based prediction modeling for titanium dioxide-Assisted photocatalytic degradation of air contaminants

Results in Engineering, Год журнала: 2024, Номер 23, С. 102637 - 102637

Опубликована: Июль 29, 2024

Airborne contaminants pose significant environmental and health challenges. Titanium dioxide (TiO2) has emerged as a leading photocatalyst in the degradation of air compared to other photocatalysts due its inherent inertness, cost-effectiveness, photostability. To assess effectiveness, laboratory examinations are frequently employed measure photocatalytic rate TiO2. However, this approach involves time-consuming requirements, labor-intensive tasks, high costs. In literature, ensemble or standalone models commonly used for assessing performance TiO2 water contaminants. Nonetheless, application metaheuristic hybrid potential be more effective predictive accuracy efficiency. Accordingly, research utilized machine learning (ML) algorithms estimate photo-degradation constants organic pollutants using nanoparticles exposure ultraviolet light. Six metaheuristics optimization algorithms, namely, nuclear reaction (NRO), differential evolution algorithm (DEA), human felicity (HFA), lightning search (LSA), Harris hawks (HHA), tunicate swarm (TSA) were combined with random forest (RF) technique establish models. A database 200 data points was acquired from experimental studies model training testing. Furthermore, multiple statistical indicators 10-fold cross-validation examine established model's robustness. The TSA-RF demonstrated superior prediction among six suggested models, achieving an impressive correlation (R) 0.90 lower root mean square error (RMSE) 0.25. contrast, HFA-RF, HHA-RF, NRO-RF exhibited slightly R-value 0.88, RMSE scores 0.32. DEA-RF LSA-RF while effective, showed marginally 0.85, values 0.45 0.44, respectively. Moreover, SHapley Additive exPlanation (SHAP) results indicated that rates through photocatalysis most notably influenced by factors such reactor sizes, dosage, humidity, intensity.

Язык: Английский

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Advancing Water Quality Assessment and Prediction Using Machine Learning Models, Coupled with Explainable Artificial Intelligence (XAI) Techniques Like Shapley Additive Explanations (SHAP) For Interpreting the Black-Box Nature

Results in Engineering, Год журнала: 2024, Номер 23, С. 102831 - 102831

Опубликована: Сен. 1, 2024

Water quality assessment and prediction play crucial roles in ensuring the sustainability safety of freshwater resources. This study aims to enhance water by integrating advanced machine learning models with XAI techniques. Traditional methods, such as index, often require extensive data collection laboratory analysis, making them resource-intensive. The weighted arithmetic index is employed alongside models, specifically RF, LightGBM, XGBoost, predict quality. models' performance was evaluated using metrics MAE, RMSE, R2, R. results demonstrated high predictive accuracy, XGBoost showing best (R2 = 0.992, R 0.996, MAE 0.825, RMSE 1.381). Additionally, SHAP were used interpret model's predictions, revealing that COD BOD are most influential factors determining quality, while electrical conductivity, chloride, nitrate had minimal impact. High dissolved oxygen levels associated lower indicative excellent pH consistently influenced predictions. findings suggest proposed approach offers a reliable interpretable method for prediction, which can significantly benefit specialists decision-makers.

Язык: Английский

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Estimating the compressive and tensile strength of basalt fibre reinforced concrete using advanced hybrid machine learning models

Structures, Год журнала: 2025, Номер 71, С. 108138 - 108138

Опубликована: Янв. 1, 2025

Язык: Английский

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Machine learning predictive insight of water pollution and groundwater quality in the Eastern Province of Saudi Arabia

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 28, 2024

This study presents an innovative approach for predicting water and groundwater quality indices (WQI GWQI) in the Eastern Province of Saudi Arabia, addressing critical challenges scarcity pollution arid regions. Recent literature highlights increasing attention towards WQI based on index (WPI) GWQI as essential tools simplifying complex hydrogeological data, thereby facilitating effective management protection. Unlike previous works, present research introduces a novel hybrid method that integrates non-parametric kernel Gaussian learning (GPR), adaptive neuro-fuzzy inference system (ANFIS), decision tree (DT) algorithms. marks first application prediction offering significant advancement field. Through laboratory analysis combination various machine (ML) techniques, this enhances capabilities, particularly unmonitored sites semi-arid The study's objectives include feature engineering dependency sensitivity to identify most influential variables affecting GWQI, development predictive models using ANFIS, GPR, DT both indices. Furthermore, it aims assess impact different data portions predictions, exploring divisions such (70% / 30%), (60% 40%), (80% 20%) training testing phase, respectively. By filling gap resource management, offers implications regions facing similar environmental challenges. its methodology comprehensive analysis, contributes broader effort managing protecting resources areas. result proved GPR-M1 exhibited exceptional phase accuracy with RMSE = 0.0169 GWQI. Similarly, WPI, ANFIS-M1 achieved high skills 0.0401. results emphasize role quantity enhancing model robustness precision assessment.

Язык: Английский

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Predicting the properties of concrete incorporating graphene nano platelets by experimental and machine learning approaches

Case Studies in Construction Materials, Год журнала: 2024, Номер 20, С. e03018 - e03018

Опубликована: Март 2, 2024

Modern infrastructure requirements necessitate structural components with improved durability and strength properties. The incorporation of nanomaterials (NMs) into concrete emerges as a viable strategy to enhance both the concrete. Nevertheless, complexities inherent in these nanoscale cementitious composites are notably intricate. Traditional regression models face constraints comprehensively capturing intricate compositions. Thus, posing challenges delivering precise dependable estimations. Therefore, current study utilized three machine learning (ML) methods, including artificial neural network (ANN), gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), conjunction experimental investigation effect integration graphene nanoplatelets (GNPs) on electrical resistivity (ER) compressive (CS) containing GNPs. Concrete GNPs demonstrated an fractional change (FCR) strength. measures depict that enhancement was notable at GNP concentrations 0.05% 0.1%, showcasing increases 13.23% 16.58%, respectively. Simultaneously, highest observed FCR reached -12.19% -13%, prediction efficacy proved be outstanding forecasting characteristics For CS, GEP, ANN, ANFIS impressive correlation coefficient (R) values 0.974, 0.963, 0.954, resistivity, exhibited high R-values 0.999, 0.995, 0.987, comparative analysis revealed GEP model delivered predictions for ER CS. mean absolute error (MAE) GEP-CS 14.51% reduction compared ANN-CS substantial 48.15% improvement over ANFIS-CS model. Similarly, displayed MAE 38.14% lower Moreover, GEP-ER 56.80% 82.47% Shapley Additive explanation (SHAP) provided curing age SHAP score. indicating its predominant contribution CS prediction. In predicting ER, content score, signifying estimation. This highlights ML's accuracy properties nanoplatelets, offering fast cost-effective alternative time-consuming experiments.

Язык: Английский

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Application of metaheuristic algorithms for compressive strength prediction of steel fiber reinforced concrete exposed to high temperatures

Materials Today Communications, Год журнала: 2024, Номер 39, С. 108832 - 108832

Опубликована: Апрель 6, 2024

Язык: Английский

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Advanced Framework for Predicting Rainfall-Runoff: Comparative Evaluation of AI Models for Enhanced Forecasting Accuracy

Water Resources Management, Год журнала: 2025, Номер unknown

Опубликована: Янв. 17, 2025

Язык: Английский

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Unveiling the Hidden Connections: Using Explainable Artificial Intelligence to Assess Water Quality Criteria in Nine Giant Rivers

Journal of Cleaner Production, Год журнала: 2025, Номер unknown, С. 144861 - 144861

Опубликована: Янв. 1, 2025

Язык: Английский

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A novel algal bloom risk assessment framework by integrating environmental factors based on explainable machine learning

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103098 - 103098

Опубликована: Март 1, 2025

Язык: Английский

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